Computationally efficient tools for fitting generalized linear model with convex or nonconvex penalty. Users can enjoy the superior statistical property of nonconvex penalty such as SCAD and MCP which has significantly less estimation error and overfitting compared to convex penalty such as lasso and ridge. Computation is handled by multistage convex relaxation and the PathwIse CAlibrated Sparse Shooting algOrithm (PICASSO) which exploits warm start initialization, active set updating, and strong rule for coordinate preselection to boost computation, and attains a linear convergence to a unique sparse local optimum with optimal statistical properties. The computation is memoryoptimized using the sparse matrix output.
Package details 


Author  Jason Ge, Xingguo Li, Mengdi Wang, Tong Zhang, Han Liu and Tuo Zhao 
Date of publication  20170720 23:04:54 UTC 
Maintainer  Jason Ge <jiange@princeton.edu> 
License  GPL2 
Version  1.0.1 
Package repository  View on CRAN 
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